The solar-reflected brightness distribution of a man-made space object has regions of spatially uniform brightness
and spectral content that are interrupted only by boundaries separating one material region from another.
The relatively simple structure of this distribution permits, as we demonstrate here, spectral-correlation-based
strategies to extract information about the boundaries and material constituents of the segments of the object
surface. Still simpler compressive-sensing (CS) based approaches that require no specific spectral analysis can
also efficiently perform such information extraction, which is a critical task of any space-object identification
(SOI) system. We analyze here these latter approaches by means of statistical information theory (IT) in the
context of a highly idealized satellite model with rectilinear material boundaries and quasi-one-dimensional (1D)
brightness distribution. Our analysis includes spectrally dependent diffractive blur as well as detector noise
against which we optimize, via our IT calculations, the choice of the CS mask set, the bandwidth of the spectral
measurements, and the minimum number of measurements needed for extracting information about the boundary
locations and material identities.